1. Field of the Invention
The present invention relates to an ultrasonic diagnosis apparatus, an image processing apparatus, a control method for the ultrasonic diagnosis apparatus, and an image processing method which reduce speckle and noise contained in volume data.
2. Description of the Related Art
An ultrasonic diagnosis apparatus applies the ultrasonic pulses generated from the transducers incorporated in an ultrasonic probe into a subject, and receives the ultrasonic waves reflected by the subject through the transducers. The ultrasonic diagnosis apparatus then generates an echo signal corresponding to the received ultrasonic waves, generates ultrasonic image data based on the generated echo signal, and displays an ultrasonic image. In general, the ultrasonic diagnosis apparatus acquires a two-dimensional ultrasonic image by scanning a cross-section of a subject using an ultrasonic probe including a plurality of transducers arrayed one-dimensionally. Some recent ultrasonic diagnosis apparatuses can acquire a three-dimensional ultrasonic image (volume data) by scanning a volume in a subject using an ultrasonic probe or the like which includes a plurality of transducers arrayed two-dimensionally.
The ultrasonic waves reflected by a plurality of nearby subject tissues interfere with each other due to their phases. This interference produces an image pattern which differs in appearance from that obtained by synthesis only amplitudes, i.e., speckle. Speckle hinders the accurate observation of the position and shape of the boundary of a subject tissue. For this reason, various types of processing methods for the reduction of speckle have been proposed.
As described in, for example, the first reference (Jpn. Pat. Appln. KOKAI Publication No. 2006-116307), there has been proposed a method of performing multiresolution analysis of an ultrasonic image by wavelet transform/inverse transform or the like, detecting an edge of the image at each level, calculating the direction of an edge for each pixel, and performing smoothing in the tangential direction of each edge and filtering for sharpening in the normal direction of each edge. However, the application of this technique in the first reference is limited to two-dimensional ultrasonic images.
A method using a structure tensor is available as a method of performing structural analysis of a pixel region in image data as disclosed in the second reference (K. Z. Abd-Elmoniem, A. M. Youssef, and Y. M. Kadah, “Real-Time Speckle Reduction and Coherence Enhancement in Ultrasound Imaging via Nonlinear Anisotropic Diffusion”, IEEE transactions on biomedical engineering, vol. 49, NO. 9, September 2002). The second reference discloses the application of a nonlinear anisotropic diffusion filter to the reduction of speckle on an ultrasonic image. However, the application of the second reference is also limited to two-dimensional ultrasonic images.
Note that it is possible to reduce speckle in volume data by dividing the volume data into cross-sections perpendicular to specific coordinate axes and applying a two-dimensional speckle reduction filter to each cross-section. In this case, however, as compared with the speckle reduction accuracy on a given surface to which a two-dimensional filter is applied, the speckle reduction accuracy on two surfaces vertically intersecting the given surface is low.
When three-dimensionally extending a nonlinear anisotropic diffusion filter, it is necessary to classify the characteristics of structures according to the magnitude relationship between the three eigenvalues of a three-dimensional structure tensor. The third reference (Z. Yu, C. Bajaj, “A Structure Tensor Approach for 3D Image Skeletonization: Applications in Protein Secondary Structure Analysis, Image Processing, 2006 IEEE International Conference on, 2006) discloses a concrete example of this technique. The third reference, however, does not disclose any method of linking each eigenvalue of a three-dimensional structure tensor to a three-dimensional diffusion equation.